Few-shot learning is an almost unexplored area in the field of medical image analysis. https://www.selleckchem.com/products/amg-193.html We propose a method for few-shot diagnosis of diseases and conditions from chest x-rays using discriminative ensemble learning. Our design involves a CNN-based coarse-learner in the first step to learn the general characteristics of chest x-rays. In the second step, we introduce a saliency-based classifier to extract disease-specific salient features from the output of the coarse-learner and classify based on the salient features. We propose a novel discriminative autoencoder ensemble to design the saliency-based classifier. The classification of the diseases is performed based on the salient features. Our algorithm proceeds through meta-training and meta-testing. During the training phase of meta-training, we train the coarse-learner. However, during the training phase of meta-testing, we train only the saliency-based classifier. Thus, our method is first-of-its-kind where the training phase of meta-training and the training phase of meta-testing are architecturally disjoint, making the method modular and easily adaptable to new tasks requiring the training of only the saliency-based classifier. Experiments show as high as ?19% improvement in terms of F1 score compared to the baseline in the diagnosis of chest x-rays from publicly available datasets.The connectional map of the baby brain undergoes dramatic changes over the first year of postnatal development, which makes its mapping a challenging task, let alone learning how to predict its evolution. Currently, learning models for predicting brain connectomic developmental trajectories remain broadly absent despite their great potential in spotting atypical neurodevelopmental disorders early. This is most likely due to the scarcity and often incompleteness of longitudinal infant neuroimaging studies for training such models. In this paper, we propose the first approach for progressively predicting longitudinal development of brain networks during the postnatal period solely from a baseline connectome around birth. To this end, a supervised multi-regression sample selection strategy is designed to learn how to identify the best set of neighbors of a testing baseline connectome to eventually predict its evolution trajectory at follow-up timepoints. However, given that the training dataset may have missing samples (connectomes) at certain timepoints, this may affect the training of the predictive model. To overcome this problem, we perform a low-rank tensor completion based on a robust principal component analysis to impute the missing training connectomes by linearly approximating similar complete training networks. In the prediction step, our sample selection strategy aims to preserve spatiotemporal relationships between consecutive timepoints. Therefore, the proposed method learns how to identify the set of the local closest neighbors to a target network by training an ensemble of bidirectional regressors leveraging temporal dependency between consecutive timepoints with a recall to the baseline observations to progressively predict the evolution of a testing network over time. Our method achieves the best prediction results and better captures the dynamic changes of each brain connectome over time in comparison to its ablated versions using leave-one-out cross-validation strategy.Clinical trials have shown oral corticosteroid (OCS) sparing effects of anti-IL5/anti-IL5-receptor treatments. The generalisability of these clinical trials may be limited, due to the rigid inclusion and exclusion criteria, and the short tapering duration. Real-world evidence is needed to bridge the gap between the clinical trials and the clinical practice. With this study we present real-life data on the OCS sparing effects of anti-IL5/anti-IL5-receptor treatments after 12 and 24 months of treatment.
Severe, eosinophilic asthma patients treated with mepolizumab, reslizumab or benralizumab for 24 months were included in this observational study. Data on OCS-dose, FEV1, ACT/ACQ score and blood eosinophils were obtained from the patients records before anti-IL5/anti-IL5-receptor treatment, and after 12 and 24 months of treatment.
At baseline 75% of patients were on daily OCS. This number was reduced to 50% after one year of treatment, p&lt;0.001, and 28% after two years of treatment, p&lt;0.001. Within the group on daily OCS the median daily dose was reduced from 10mg of Prednisolone at baseline (IQR 5-20) to 3.75mg Prednisolone (IQR 0-10) after 12 months, and 0mg Prednisolone (IQR 0-7.5) after 24 months, p&lt;0.001.
The findings in this study add to the generalisability of the clinical studies, showing significant OCS sparing effects of anti-IL5/anti-IL5-receptor treatment in a real-life setting. Furthermore, these findings add to the understanding of the long-term effects of anti-IL5/anti-IL5-receptor treatment, showing an even further and persistent OCS reduction after two years of treatment.
The findings in this study add to the generalisability of the clinical studies, showing significant OCS sparing effects of anti-IL5/anti-IL5-receptor treatment in a real-life setting. Furthermore, these findings add to the understanding of the long-term effects of anti-IL5/anti-IL5-receptor treatment, showing an even further and persistent OCS reduction after two years of treatment.Some lactic acid bacteria (LAB) isolated from beer or wine produce capsular β-glucans from UDP-glucose via the membrane-anchored glycosyltransferase GTF-2. This phenomenon is feared in breweries, because the viscosity of the affected liquids drastically increases due to the β-glucan and concomitant pellicle formation of these LAB. Currently it is unknown if this type of polysaccharide formation provides any advantage for the producing LAB during the colonization of (ethanol-containing) liquids. We thus used the β-glucan producer Levilactobacillus (L.) brevis TMW 1.2112 and its β-glucan-deficient transposon mutant (Δ gtf-2), and compared their growth at different ethanol concentrations and their competitiveness during co-cultivation. No significant inhibition in growth and differences in acidification were observed for both strains up to ethanol concentrations of 8% (v/v). At 10 % ethanol, the β-glucan forming wildtype increased its cell number and produced more acid in comparison to the mutant strain, which settled at the bottom of the fermentation tubes at any tested condition.